LA News

New PhD Thesis

Luca successfully passed his PhD exam on January 25th, 2019.

EPFL Thesis 9264: Model-based predictive control methods for distributed energy resources in smart grids by Luca Fabietti.

Thesis Directors: Colin Jones

New PhD Thesis

Predrag successfully passed his PhD exam on September 21th, 2018.
EPFL Thesis 257129: Real-Time Optimization of Interconnected Systems via Modifier Adaptation, with Application to Gas-Compressor Stations by Predrag Milosavljevic;

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11:15

12:15

ME C2 405

A Set Membership approach to the identification of linear systems with guaranteed simulation accuracy.

Abstract: The identification of models of linear systems having guaranteed simulation accuracy is of great importance in all the cases where a model and a measure of its uncertainty are needed for long range prediction or simulation purpose, like in robust Model Predictive Control. In this talk, I will address the problem of model identification for linear systems affected by a bounded additive disturbance, where the bound is unknown, and a finite set of sampled data is available for model identification. The objective is the identification of one-step-ahead models, and the estimation of their accuracy by means of worst-case simulation error bounds, resorting to the Set Membership identification framework. I will present new results that allow to develop a procedure for the estimation of the unknown disturbance bound and of the system decay rate from data. Then, the available data and the estimated disturbance bound are used to define the set of all the possible models that are compatible with data and with the estimated quantities. The estimated decay rate is used to refine the standard Feasible Parameter Set (FPS) formulation, by adding constraints that enforce a converging behavior of the iterated models. Finally, the desired one-step-ahead model is identified by numerical optimization, and the worst-case error bound related to the obtained model is calculated over the available data and FPSs. The performance and the validity of the proposed approach are evaluated over numerical simulations and a real world experimental case study.

Bio: Marco Lauricella received the B.Sc. and M.Sc. degrees in Automation and Control Engineering from Politecnico di Milano, Italy, in 2013 and 2015, respectively. He is currently a PhD Fellow in Information Engineering at the Department of Electronic, Information and Bio-engineering of Politecnico di Milano, Italy. His research interests include system identification and fault detection, and their applications to electric systems.

By: Marco Lauricella, Politecnico di Milano

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14:00

14:45

ME C2 405

Intermodal Autonomous Mobility-on-Demand

Abstract: We study models and coordination policies for intermodal Autonomous Mobility-on-Demand (AMoD), wherein a fleet of self-driving vehicles provides on-demand mobility jointly with public transit.
Specifically, we first present a network flow model for intermodal AMoD, where we capture the coupling between AMoD and public transit and the goal is to maximize social welfare. Second, leveraging such a model, we design a pricing and tolling scheme that allows to achieve the social optimum under the assumption of a perfect market with selfish agents. Finally, we present a real-world case study for New York City. Our results show that the coordination between AMoD fleets and public transit can yield significant benefits compared to an AMoD system operating in isolation.

Bio: Mauro Salazar was born in Zürich, Switzerland. He received the B.Sc. degree in mechanical engineering from ETH Zürich in 2013, and the M.Sc. degree in mechanical engineering in 2015. He did his master thesis at EPFL with Prof. Colin Jones on the topic Low Energy Control. He is pursuing a Ph.D. degree with the Institute for Dynamic Systems and Control, ETH Zürich, under the supervision of Prof. Chris Onder. From January to July 2018, he was a visiting research scholar at the Autonomous Systems Lab, Stanford University, under the supervision of Prof. Marco Pavone.
His current research interests include optimal control theory, hybrid electric vehicles, autonomous mobility-on-demand and model predictive control.
He received the Outstanding Bachelor Award and the Excellence Scholarship and Opportunity Award from ETH Zürich. His master thesis was awarded the ETH Medal.

By: Mauro Salazar, Insitute for Dynamic Systems and Control

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ME C2 405

A Hybrid Control Framework for Accelerated Methods with Exponential Rate

Abstract: Ordinary differential equations, and in general a dynamical system viewpoint, have seen a resurgence of interest in developing fast optimization methods, mainly thanks to the availability of well-established analysis tools.

In this talk, I will provide an overview of fast algorithms and recent results from a dynamical systems perspective. I will then describe a hybrid control framework to design a class of fast gradient-based methods in continuous-time that, in comparison with the existing literature including Nesterov’s fast-gradient method, features a state-dependent, time-invariant damping term that acts as a feedback control input. The proposed design scheme allows for a user-defined, exponential rate of convergence for a class of nonconvex, unconstrained optimization problems. Finally, I will introduce a discretization method such that the resulting discrete dynamical system possesses an exponential rate of convergence.

Bio: Dr. Tamas Keviczky is an Associate Professor in Networked Cyber-Physical Systems at the Delft Center for Systems and Control, TU Delft. He was a Postdoctoral Scholar at the California Institute of Technology and received his PhD in Control Science and Dynamical Systems from the University of Minnesota. He was awarded the AACC O. Hugo Schuck Best Paper Award for Practice. He has served as an Associate Editor of Automatica since 2011 and has published over 100 scientific articles. His main research interests include distributed optimization and optimal control, model predictive control, embedded optimization-based control and estimation of large-scale systems with applications in aerospace, automotive and mobile robotics, industrial processes, and infrastructure systems such as water, heat, and electricity networks.
 

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ME C2 405

Distributed Monitoring and Fault-Tolerant Control: Scalable Tools & Industry 4.0 Perspective

Abstract: This lecture deals with a class of systems that are becoming ubiquitous in the current and future "distributed world" made by countless "nodes", which can be cities, computers, people, etc., and interconnected by a dense web of transportation, communication, or social ties. The term "network", describing such a collection of nodes and links, nowadays has become commonplace thanks to our extensive reliance on "connections of interdependent systems" in our everyday life, for building complex technical systems, infrastructures and so on. In an increasingly "smarter" planet, it is expected that such interconnected systems will be safe, reliable, available 24/7, and of low-cost maintenance – the Industry 4.0 vision. Therefore, health monitoring, fault diagnosis and fault-tolerant control are of customary importance to ensure high levels of safety, performance, reliability, dependability, and availability. In the lecture, the process industry I considered as a paradigmatic context in which, faults and malfunctions can result in off-specification production, increased operating costs, production line shutdown, danger conditions for humans, detrimental environmental impact, and so on. Faults and malfunctions need to be detected promptly and their source and severity should be diagnosed so that corrective actions can be taken as soon as possible. Once a fault is detected, the faulty subsystem can be unplugged to avoid the propagation of the fault in the interconnected large-scale system. Analogously, once the issue has been solved, the disconnected subsystem can be re-plugged-in.

In the talk, an adaptive approximation-based distributed fault diagnosis approach for large-scale nonlinear systems will be dealt with, by exploiting a "divide et impera" approach in which the overall diagnosis problem is decomposed into smaller sub-problems, which can be solved within “local” computation architectures. The distributed detection, isolation and identification task is broken down and assigned to a network of "Local Diagnostic Units", each having a "local view" of the system.

 Moreover, the lecture will address the integration of a distributed model predictive control scheme and a distributed fault diagnosis architecture. Specifically, in the off-line control design phase we adopt a decentralized algorithm and we assume that the design of a local controller can use information at most from parents of the corresponding subsystem, i.e., subsystems that influence its dynamics. This implies that the whole model of the large-scale system is never used in any step of the design process. This approach has several advantages in terms of scalability: i) the communication flow at the design phase has the same topology of the coupling graph - usually sparse - ii) the local design of controllers and fault detectors can be conducted independently; iii) local design complexity scales with the number of parent subsystems only; iv) if a subsystem joins/leaves an existing network (plug-in/unplugging operation) at most children/parents subsystems have to retune their controllers and fault detectors. We refer to this kind of decentralized synthesis as plug & play design, if - in addition - the plug-in and unplugging operations can be performed through a procedure for automatically assessing whether the operation does not spoil stability and constraint satisfaction for the overall large-scale system.
 
In the lecture, the connection is finally worked out with Virtual Commissioning which is the very recent trend in the process industry to make the dream of plug & work installation of a reliable and efficient automation system become a reality.

Bio: Thomas Parisini received the Ph.D. degree in Electronic Engineering and Computer Science in 1993 from the University of Genoa. He was with Politecnico di Milano and since 2010 he holds the Chair of Industrial Control and is Director of Research at Imperial College London. He is a Deputy Director of the KIOS Research and Innovation Centre of Excellence, University of Cyprus. Since 2001 he is also Danieli Endowed Chair of Automation Engineering with University of Trieste. In 2009-2012 he was Deputy Rector of University of Trieste. He authored or co-authored more than 300 research papers in archival journals, book chapters, and international conference proceedings. His research interests include neural-network approximations for optimal control problems, fault diagnosis for nonlinear and distributed systems, nonlinear model predictive control systems and nonlinear estimation. He is a co-recipient of the IFAC Best Application Paper Prize of the Journal of Process Control, Elsevier, for the three-year period 2011-2013 and of the 2004 Outstanding Paper Award of the IEEE Trans. on Neural Networks. He is also a recipient of the 2007 IEEE Distinguished Member Award. In 2016, he was awarded as Principal Investigator at Imperial of the H2020 European Union flagship Teaming Project KIOS Research and Innovation Centre of Excellence led by University of Cyprus with an overall budget of over 40 MEuro. In 2012, he was awarded an ABB Research Grant dealing with energy-autonomous sensor networks for self-monitoring industrial environments. Thomas Parisini currently serves as Vice-President for Publications Activities of the IEEE Control Systems Society and during 2009-2016 he was the Editor-in-Chief of the IEEE Trans. on Control Systems Technology. Since 2017, he is Editor for Control Applications of Automatica and since 2018 he is the Editor in Chief of the European Journal of Control.
He is also the Chair of the IFAC Technical Committee on Fault Detection, Supervision & Safety of Technical Processes - SAFEPROCESS.  He was the Chair of the IEEE Control Systems Society Conference Editorial Board and a Distinguished Lecturer of the IEEE Control Systems Society. He was an elected member of the Board of Governors of the IEEE Control Systems Society and of the European Control Association (EUCA) and a member of the board of evaluators of the 7th Framework ICT Research Program of the European Union. Thomas Parisini is currently serving as an Associate Editor of the Int. J. of Control and served as Associate Editor of the IEEE Trans. on Automatic Control, of the IEEE Trans. on Neural Networks, of Automatica, and of the Int. J. of Robust and Nonlinear Control.  Among other activities, he was the Program Chair of the 2008 IEEE Conference on Decision and Control and General Co-Chair of the 2013 IEEE Conference on Decision and Control. Prof. Parisini is a Fellow of the IEEE and of the IFAC.
 

By: Thomas Parisini, Imperial College London & University of Trieste

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MED 2 1522

Distributed energy systems - An optimal design perspective

Abstract:
Distributed energy systems (DES) can sustainably transform the energy supply of buildings and districts by incorporating multiple efficient energy technologies and locally available renewable sources. This talk will focus on the model-based optimal design of DES and will highlight recent research developments in the field. First, the ways that mathematical optimisation can assist with the task of optimal DES design will be introduced. Then, approaches to represent the different energy technologies with varying level of detail in optimisation models will be presented. The necessary interactions with other modelling domains, such as Building Performance Simulation, will also be discussed. Additionally, ways to address challenges like uncertainty and the computational complexity when extending DES design models at larger scales like the whole city scale will also be presented. Finally, going beyond modelling, the talk will conclude by highlighting insights from real-world projects that have investigated the design of DES.
 
Bio:
Georgios Mavromatidis is a post-doctoral researcher at the Chair of Building Physics, ETH Zurich and he is also affiliated with the Laboratory for Urban Energy Systems at Empa Dübendorf.  Dr. Mavromatidis received his PhD degree in 2017 from ETH Zurich, Switzerland and his thesis focused on the design of distributed energy systems under uncertainty. Previously, in 2012, he obtained an MSc in Sustainable Energy Futures from Imperial College London, UK, and, in 2010, a Diploma in Mechanical Engineering from Aristotle University of Thessaloniki, Greece. His research interests include the optimal design of distributed multi-energy systems for buildings and districts, uncertainty in energy system design, and the interactions between building retrofits and upgrades of building energy systems. Additional information can be found on his personal website: www.mavromatidis.me